In radiotherapy or radiosurgery, treatment planning is typically performed based on medical images of a patient and requires the delineation of target volumes and normal critical organs in the medical images. Thus, segmentation of anatomical structures in medical images is a prerequisite and important step for radiotherapy treatment planning. Accurate and automatic computer-based segmentation or contouring of anatomical structures can facilitate the design and/or adaptation of an optimal treatment plan. However, accurate and automatic segmentation of medical images currently remains a challenging task because of deformation and variability of the shapes, sizes, positions, etc. of the target volumes and critical organs in different patients.
FIG. 1 illustrates an exemplary three-dimensional (3D) computed tomography (CT) image from a typical prostate cancer patient. Illustration (A) shows a pelvic region of the patient in a 3D view, which includes the patient's bladder, prostate, and rectum. Images (B), (C), and (D) are axial, sagittal, and coronal views from a 3D CT image of this pelvic region. As shown in images (B), (C), and (D), most part of the patient's prostate boundary is not visible. That is, one cannot readily distinguish the prostate from other anatomical structures or determine a contour for the prostate. In comparison, images (E), (F), and (G) show the expected prostate contour on the same 3D CT image. As illustrated in FIG. 1, conventional image segmentation methods solely based on contrast and textures presented in the image would likely fail when used to segment this exemplary 3D CT image. Thus, various approaches are proposed to improve the accuracy of automatic segmentation of medical images.
For example, atlas-based auto-segmentation (ABAS) methods have been used to tackle the problem of contouring anatomical structures in radiotherapy treatment planning. ABAS methods map contours in a new image based on a previously defined anatomy configuration in a reference image, i.e., the atlas. The accuracy of ABAS methods largely depends on the performance of atlas registration methods. As discussed above, the shapes and sizes of some organs may vary for different patients, and may be deformed in large scales at different stages for the same patient, which may decrease the registration accuracy and affect the automatic segmentation performed by ABAS methods.
Recent developments in machine learning techniques make improved image segmentation, such as more accurate segmentation of low-contrast parts in images or lower quality images. For example, various machine learning algorithms can “train” the machines, computers, or computer programs to predict (e.g., by estimating the likelihood of) the anatomical structure each pixel or voxel of a medical image represents. Such prediction or estimation usually uses one or more features of the medical image as input. Therefore, the performance of the segmentation highly depends on the types of features available. For example, Random Forest (RF) method has been used for image segmentation purpose with some success. A RF model can be built based on extracting different features from a set of training samples. However, the features employed in the RF method require to be designed manually and are specific for contouring one-type of organ. It is tedious and time-consuming to design an optimal combination of features for different segmentation applications.
Accordingly, there is a need for new automatic segmentation methods to improve segmentation performance on medical images in radiation therapy or related fields.